Search Results (2,351)

Background: Breast cancer, the most prevalent malignancy among women worldwide, exhibits significant heterogeneity, particularly in the tumor microenvironment (TME), which poses challenges for treatment. Spatial transcriptomics (ST) has emerged as a transformative technology, enabling gene expression analysis while preserving tissue spatial architecture. This provides unprecedented insights into tumor heterogeneity, cellular interactions, and disease mechanisms, offering a powerful tool for advancing breast cancer research and therapy. This review aims to synthesize the applications of ST in breast cancer research, focusing on its role in decoding tumor heterogeneity, characterizing the TME, elucidating progression and metastasis dynamics, and predicting therapeutic responses. We also explore how ST can bridge molecular profiling with clinical translation to enhance precision therapy. The key scientific concepts of review included the following: We summarize the technological advancements in ST, including imaging-based and sequencing-based methods, and their applications in breast cancer. Key findings highlight how ST resolves spatial heterogeneity across molecular subtypes and histological variants. ST reveals the dynamic interplay between tumor cells, immune cells, and stromal components, uncovering mechanisms of immune evasion, metabolic reprogramming, and therapeutic resistance. Additionally, ST identifies spatial prognostic markers and predicts responses to chemotherapy, targeted therapy, and immunotherapy. We propose that ST serves as a hub for integrating multi-omics data, offering a roadmap for precision oncology and personalized treatment strategies in breast cancer. Full article

Milk is a nutritionally rich food and a frequent target of economically motivated adulteration, particularly through substitution with lower-cost milk types. Over the past decade, significant progress has been made in the authentication of milk using advanced proteomic and chemometric approaches, with a focus on the discovery and application of protein and peptide biomarkers for species differentiation and fraud detection. Recent innovations in both top-down and bottom-up proteomics have markedly improved the sensitivity and specificity of detecting key molecular targets, including caseins and whey proteins. Peptide-based methods are especially valuable in processed dairy products due to their thermal stability and resilience to harsh treatment, although their species specificity may be limited when sequences are conserved across related species. Robust chemometric approaches are increasingly integrated with proteomic pipelines to handle high-dimensional datasets and enhance classification performance. Multivariate techniques, such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), are frequently employed to extract discriminatory features and model adulteration scenarios. Despite these advances, key challenges persist, including the lack of standardized protocols, variability in sample preparation, and the need for broader validation across breeds, geographies, and production systems. Future progress will depend on the convergence of high-resolution proteomics with multi-omics integration, structured data fusion, and machine learning frameworks, enabling scalable, specific, and robust solutions for milk authentication in increasingly complex food systems. Full article

Inflammatory Bowel Disease (IBD), which includes ulcerative colitis (UC) and Crohn’s disease (CD), results from dysregulated immune responses that drive chronic intestinal inflammation. Neutrophils, as key effectors of the innate immune system, contribute to IBD through multiple mechanisms, including the release of reactive oxygen species (ROS), pro-inflammatory cytokines, and neutrophil extracellular traps (NETs). NETs are web-like structures composed of DNA, histones, and associated proteins including proteolytic enzymes and antimicrobial peptides. NET formation is increased in IBD and has a context-dependent role; under controlled conditions, NETs support antimicrobial defense and tissue repair, whereas excessive or dysregulated NETosis contributes to epithelial injury, barrier disruption, microbial imbalance, and thrombotic risk. This review examines the roles of neutrophils and NETs in IBD. We summarize recent single-cell and spatial-omics studies that reveal extensive neutrophil heterogeneity in the inflamed gut. We then address the dual role of neutrophils in promoting tissue damage—through cytokine release, immune cell recruitment, ROS production, and NET formation—and in supporting microbial clearance and mucosal healing. We also analyze the molecular mechanisms regulating NETosis, as well as the pathways involved in NET degradation and clearance. Focus is given to the ways in which NETs disrupt the epithelial barrier, remodel the extracellular matrix, contribute to thrombosis, and influence the gut microbiota. Finally, we discuss emerging therapeutic strategies aimed at restoring NET homeostasis—such as PAD4 inhibitors, NADPH oxidase and ROS pathway modulators, and DNase I—while emphasizing the need to preserve antimicrobial host defenses. Understanding neutrophil heterogeneity and NET-related functions may facilitate the development of new therapies and biomarkers for IBD, requiring improved detection tools and integrated multi-omics and clinical data. Full article

Ischemic stroke is a complex, multifactorial disorder with a significant heritable component. Recent developments in genome-wide association studies (GWASs) have identified several common variants associated with clinical outcomes, stroke subtypes, and overall risk. Key loci implicated in biological pathways related to vascular integrity, lipid metabolism, inflammation, and atherogenesis include 9p21 (ANRIL), HDAC9, SORT1, and PITX2. Although polygenic risk scores (PRSs) hold promise for early risk prediction and stratification, their clinical utility remains limited by Eurocentric bias and missing heritability. Integrating multiomics approaches, such as functional genomics, transcriptomics, and epigenomics, enhances our understanding of stroke pathophysiology and paves the way for precision medicine. This review summarizes the current genetic landscape of ischemic stroke, emphasizing how evolving methodologies are shaping its prevention, diagnosis, and treatment. Full article

Background: Urinary incontinence (UI) and depression are prevalent conditions affecting millions globally and are significantly associated with various demographic, health, and socio-economic factors. This study examines the associations between UI and depression over a 14-year period using nationwide data. Methods: We analyzed cross-sectional data from the Turkish Health Studies Surveys conducted in seven different years between 2008 and 2022, including 125,276 participants aged 15 and older and excluding those with incomplete key health data. Variables included chronic conditions, BMI, depression severity (assessed by PHQ-8), socio-economic status, and lifestyle factors. Univariable and multivariable logistic regression models were used to investigate associations between UI and various risk factors over time. Results: The prevalence of UI and depression fluctuated over the 14 years, with a significant increase observed in 2014. Multivariate analysis confirmed a strong and consistent association between UI and depression across genders and age groups, even after adjusting for confounders. Higher depression severity increased the odds of experiencing UI. Age, multiple comorbidities, higher BMI, and lower socio-economic status were associated with an increased likelihood of UI. Obesity was a significant risk factor for UI in females but not in males. Urban living and higher education levels were inversely associated with UI. The simultaneous rise in UI and depression in 2014 may be linked to socio-economic changes during that period. Conclusions: The findings suggest a robust link between UI and depression, influenced by a complex interplay of health, demographic, and socio-economic factors, needing prospective studies to further investigate the causal pathway of these associations. Full article

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that primarily affects motor neurons, leading to loss of muscle control, and, ultimately, respiratory failure and death. Despite some advances in recent years, the underlying genetic and molecular mechanisms of ALS remain largely elusive. In this respect, a better understanding of these mechanisms is needed to identify new and biologically relevant therapeutic targets that could be developed into treatments that are truly disease-modifying, in that they address the underlying causes rather than the symptoms of ALS. In this study, we used two approaches to model multi-omics data in order to map and elucidate the genetic and molecular mechanisms involved in ALS, i.e., the molecular landscape building approach and the Patrimony platform. These two methods are complementary because they rely upon different omics data sets, analytic methods, and scoring systems to identify and rank therapeutic target candidates. The orthogonal combination of the two modeling approaches led to significant convergences, as well as some complementarity, both for validating existing therapeutic targets and identifying novel targets. As for validating existing targets, we found that, out of 217 different targets that have been or are being investigated for drug development, 10 have high scores in both the landscape and Patrimony models, suggesting that they are highly relevant for ALS. Moreover, through both models, we identified or corroborated novel putative drug targets for ALS. A notable example of such a target is MATR3, a protein that has strong genetic, molecular, and functional links with ALS pathology. In conclusion, by using two distinct and highly complementary disease modeling approaches, this study enhances our understanding of ALS pathogenesis and provides a framework for prioritizing new therapeutic targets. Moreover, our findings underscore the potential of leveraging multi-omics analyses to improve target discovery and accelerate the development of effective treatments for ALS, and potentially other related complex human diseases. Full article

Sporadic colorectal cancer (CRC), the third leading cause of cancer-related death globally, arises through a continuum from normal tissue to adenomas, progressing from low-grade (LGD) to high-grade dysplasia (HGD); yet, the early epigenetic drivers of this transition remain unclear. To investigate these events, we profiled LGD and HGD adenomas using EM-seq, and identified a consensus differential methylation signature (DMS) of 626 regions through two independent bioinformatics pipelines. This signature effectively distinguished LGD from HGD in both tissue and plasma-derived cell-free DNA (cfDNA), highlighting specific methylation patterns. Functional annotation indicated enrichment for regulatory elements associated with transcription factor activity and cell signaling. Applying the DMS to the TCGA CRC dataset revealed three tumor subtypes with increasing hypermethylation and one normal cluster. The most hypermethylated subtype exhibited poor survival, high mutation burden, and disrupted transcriptional networks. While overlapping with classical CpG Island Methylator Phenotype (CIMP) categories, the DMS captured a broader spectrum of methylation alterations. These findings suggest that the DMS captures functionally relevant, antecedent epigenetic alterations in CRC progression, enabling the robust stratification of dysplasia severity and tumor subtypes. This signature holds promise for enhancing preclinical detection and molecular classification, and warrants further evaluation in larger prospective cohorts. Full article

Background: Boiling histotripsy (BH) uses high-amplitude, short-pulse focused ultrasound to disrupt tissue mechanically. Oncolytic virotherapy using reovirus has shown modest clinical benefit in pancreatic cancer patients. Here, reovirus and BH were used to treat pancreatic tumours, and their effects on the immune transcriptome of these tumours were characterised. Methods: Orthotopic syngeneic murine pancreatic KPC tumours grown in immune-competent subjects, were allocated to control, reovirus, BH and combined BH and reovirus treatment groups. Acoustic cavitation was monitored using a passive broadband cavitation sensor. Treatment effects were assessed histologically with hematoxylin and eosin staining. Single-cell multi-omics combining whole-transcriptome analysis with the expression of surface-expressed immune proteins was used to assess the effects of treatments on tumoural leukocytes. Results: Acoustic cavitation was detected in all subjects exposed to BH, causing cellular disruption in tumours 6 h after treatment. Distinct cell clusters were identified in the pancreatic tumours 24 h post-treatment. These included neutrophils and cytotoxic T cells overexpressing genes associated with an N2-like and an exhaustion phenotype, respectively. Reovirus decreased macrophages, and BH decreased regulatory T cells compared to controls. The combined treatments increased neutrophils and the ratio of various immune cells to Treg. All treatments overexpressed genes associated with an innate immune response, while ultrasound treatments downregulated genes associated with the transporter associated with antigen processing (TAP) complex. Conclusions: Our results show that the combined BH and reovirus treatments maximise the overexpression of genes associated with the innate immune response compared to that seen with each individual treatment, and illustrate the anti-immune phenotype of key immune cells in the pancreatic tumour microenvironment. Full article

Leymus chinensis (Trin.) Tzvel., a vital native forage grass in northern China for ecological restoration and livestock production, faces severe yield losses and grassland degradation due to rust (Puccinia spp.) infection. Current control strategies, reliant on chemical interventions, are limited by evolving resistance risks and environmental concerns, while rust-resistant breeding remains hindered by insufficient molecular insights. To address this, we systematically evaluated rust resistance in 24 L. chinensis germplasms from diverse geographic origins, identifying six highly resistant (HR) and five extremely susceptible (ES) genotypes. Integrating transcriptomics and metabolomics, we dissected molecular responses to Puccinia infection, focusing on contrasting HR (Lc71) and ES (Lc5) germplasms at 48 h post-inoculation. Transcriptomic analysis revealed 1012 differentially expressed genes (DEGs: 247 upregulated, 765 downregulated), with enrichment in cell wall biosynthesis and photosynthesis pathways but suppression of flavonoid synthesis. Metabolomic profiling identified 287 differentially accumulated metabolites (DAMs: 133 upregulated, 188 downregulated), showing significant downregulation of pterocarpans and flavonoids in HR germplasms, alongside upregulated cutin synthesis-related metabolites. Multi-omics integration uncovered 79 co-enriched pathways, pinpointing critical regulatory networks: (1) In the nucleotide metabolism pathway, genes Lc5Ns011910, Lc1Xm057211, and Lc4Xm043884 exhibited negative cor-relations with metabolites Deoxycytidine and Cytosine. (2) In flavonoid biosynthesis, Lc2Xm054924, Lc4Xm044161, novel.8850, Lc2Ns006303, and Lc7Ns021884 were linked to naringenin and naringenin-7-O-glucoside accumulation. These candidate genes likely orchestrate rust resistance mechanisms in L. chinensis. Our findings advance the molecular understanding of rust resistance and provide actionable targets for breeding resilient germplasms. Full article

The Epstein–Barr virus (EBV) is a prevalent virus linked to various diseases, including infectious mononucleosis (IM), nasopharyngeal carcinoma, and Hodgkin’s lymphoma. Over the past few decades, EBV diagnostic strategies have evolved significantly—progressing from traditional serological assays and histopathology to more sensitive and specific molecular techniques such as nucleic acid amplification and high-throughput sequencing (HTS). While conventional methods remain valuable for their accessibility and established clinical use, they are often limited by sensitivity, speed, and multiplexing capability. In contrast, emerging technologies, including isothermal amplification, Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-based diagnostics, multi-omics integration, and AI-assisted analysis, have demonstrated great promise in improving diagnostic accuracy, speed, and applicability in diverse clinical settings, including point-of-care testing (POCT). This review systematically explores the historical development of EBV diagnostic technologies, highlighting key milestones and future trends in precision medicine and global health readiness. Full article

Heat shock protein 90 (HSP90) is a molecular chaperone that plays a pivotal role in the stabilization and functional activation of numerous oncoproteins and signaling molecules essential for cancer cell survival and proliferation. Despite the extensive development and clinical evaluation of HSP90 inhibitors, their therapeutic potential as monotherapies has been limited by suboptimal efficacy, dose-limiting toxicity, and the emergence of drug resistance. Recent studies have demonstrated that combination therapies involving HSP90 inhibitors and other anticancer agents such as chemotherapeutics, targeted therapies, and immune checkpoint inhibitors can enhance anticancer activity, overcome resistance mechanisms, and modulate the tumor microenvironment. These synergistic effects are mediated by the concurrent degradation of client proteins, the disruption of signaling pathways, and the enhancement of antitumor immunity. However, the successful clinical implementation of such combination strategies requires the careful optimization of dosage, administration schedules, toxicity management, and patient selection based on predictive biomarkers. In this review, we provide a comprehensive overview of the mechanistic rationale, preclinical and clinical evidence, and therapeutic challenges associated with HSP90 inhibitor-based combination therapies. We also discuss future directions leveraging emerging technologies including multi-omics profiling, artificial intelligence, and nanoparticle-mediated delivery for the development of personalized and effective combination regimens in oncology. Full article

This study integrated non-targeted metabolomics and transcriptomics to investigate dynamic changes in Antheraea pernyi pupae across five developmental stages. Metabolomic analysis identified 1246 metabolites, primarily organic acids, lipids, heterocyclic compounds, and oxygen-containing organics. Principal component analysis revealed stage-specific metabolic profiles: amino acid derivatives (pyruvate, proline, lysine) declined, while pyrimidines (cytidine, uridine, β-alanine) and monosaccharides (glucose, mannose) increased. 18β-glycyrrhetinic and ursolic acids accumulated significantly in the middle and late stages. Transcriptomic analysis identified 7230 differentially expressed genes (DEGs), with 366, 1705, and 5159 significantly differentially expressed genes in the T1, T3, and T5 comparison groups, respectively. KEGG enrichment highlighted ABC transporters, amino acid/pyrimidine metabolism, and tyrosine pathways as developmentally critical, with aminoacyl-tRNA biosynthesis upregulated in later phases. Integrated multi-omics analysis revealed coordinated shifts in metabolites and genes across developmental phases, reflecting dynamic nutrient remodeling during pupal maturation. This study systematically delineates the molecular transitions driving pupal development in Antheraea pernyi pupae, uncovering conserved pathway interactions and mechanistic insights into nutrient metabolism. These findings provide a scientific foundation for leveraging pupal resources in functional food innovation and bioactive compound discovery for pharmaceutical applications. Full article

To elucidate the interactions among fatty acid metabolism, immune status, and gut microbiota, both cultured and wild Coilia nasus from the Yangtze River were examined in China. The results demonstrated that wild C. nasus exhibited markedly higher lipid and docosahexaenoic acid (DHA) contents, a greater ratio of total ω-3 PUFAs to total ω-6 PUFAs, and more active antioxidant enzymes compared to cultured C. nasus. However, the shear force, water-holding capacity, and total n-6 PUFA content were lower in wild C. nasus. Transcriptome analysis revealed distinct gene expression patterns: wild C. nasus upregulated immune-related genes, while cultured C. nasus downregulated genes related to fatty acid metabolism. Significant differences were observed in alpha and beta diversity between cultured and wild groups. LEfSe analysis identified Clostridium_T, Escherichia, and Glutamicibacter as biomarkers for cultured C. nasus, while eight genera, including Pseudomonas_E and Sphingomonas_L, were predominant in wild C. nasus. Modular analysis identified five modules linked to immune functions and fatty acid metabolism. Clostridium_T, Sphingomonas_L, and Pseudomonas_E were dominant in the first two modules, with Pseudomonas_E and Clostridium_T as key regulators of fatty acid metabolism and immune processes. These differences, likely due to gut microbiota variations, provide insights for C. nasus nutritional studies. Full article

Forensic entomotoxicology is a subdiscipline that utilizes necrophagous insects as bioindicators for detecting drugs and toxicants in decomposed remains, particularly in cases where conventional biological matrices are no longer available. Toxic substances can profoundly alter insect development, physiology, and community succession, potentially impacting the accuracy of postmortem interval (PMI) estimation. This review systematically summarizes the effects of various xenobiotics, including pesticides, illicit drugs, sedatives, heavy metals, and antibiotics on larval growth, physiological traits, and gut microbial composition in forensically relevant flies. However, most studies to date have relied primarily on phenotypic observations, with limited insight into underlying molecular mechanisms. Significant interspecies and dose-dependent variability also exists in the absorption, metabolism, and physiological responses to xenobiotics. We highlight recent advances in multi-omics technologies that facilitate the identification of molecular biomarkers associated with xenobiotic exposure, particularly within the insect detoxification system. Key components such as cytochrome P450 monooxygenases (P450s), glutathione S-transferases (GSTs), and ATP-binding cassette (ABC) transporters play essential roles in xenobiotic metabolism and insecticide resistance. Additionally, the insect fat body serves as a central hub for detoxification, hormonal regulation, and energy metabolism. It integrates signals related to xenobiotic exposure and modulates larval development, making it a promising model for future mechanistic studies in insect toxicology. Altogether, this review offers a comprehensive and reliable framework for understanding the complex interactions between toxic substance exposure, insect ecology, and decomposition in forensic investigations. Full article

Traditional Chinese medicine (TCM), a holistic medical system rooted in dialectical theories and natural product-based therapies, has served as a cornerstone of healthcare systems for millennia. While its empirical efficacy is widely recognized, the polypharmacological mechanisms stemming from its multi-component nature remain poorly characterized. The conventional trial-and-error approaches for bioactive compound screening from herbs raise sustainability concerns, including excessive resource consumption and suboptimal temporal efficiency. The integration of artificial intelligence (AI) and multi-omics technologies with network pharmacology (NP) has emerged as a transformative methodology aligned with TCM’s inherent “multi-component, multi-target, multi-pathway” therapeutic characteristics. This convergent review provides a computational framework to decode complex bioactive compound–target–pathway networks through two synergistic strategies, (i) NP-driven dynamics interaction network modeling and (ii) AI-enhanced multi-omics data mining, thereby accelerating drug discovery and reducing experimental costs. Our analysis of 7288 publications systematically maps NP-AI–omics integration workflows for natural product screening. The proposed framework enables sustainable drug discovery through data-driven compound prioritization, systematic repurposing of herbal formulations via mechanism-based validation, and the development of evidence-based novel TCM prescriptions. This paradigm bridges empirical TCM knowledge with mechanism-driven precision medicine, offering a theoretical basis for reconciling traditional medicine with modern pharmaceutical innovation. Full article